Price Signals and Information

CENTRAL CHALLENGE: How to provide the incentives for emissions reduction through robust price signal and effective provision of information?

One of the four schemes tested during the FY 2016 pilot was purely informational and the other three received an energy report in addition to the financial incentives. Prior to the pilot, buildings were able to run a report showing energy use and cost, but this information did not affect energy use decisions due to several factors: none of this information was provided with context (e.g., in comparison to past energy use or other buildings with similar characteristics); it was not compiled in an easy-to-read, one-page document; and it was not dispatched to the relevant decision-makers.

In an effort to address these issues, a new monthly energy report was sent to each pilot building with information on current energy consumption, carbon emissions and carbon charge compared to historical trends. The report also included monthly energy saving tips, best performing pilot buildings for the month, and carbon equivalency to help participants understand their carbon footprint (Figure 4). Quantitative results from the pilot and comments from the pilot participants indicate that this informational report was helpful in monitoring energy use, carbon performance, and, in some cases, identifying energy use issues. However, some participants found it confusing, especially since, in an attempt to integrate feedback and continuously improve on the report, the Pilot team updated the report template several times, thereby changing expectations of what the report included and how that information was presented. And compiling the reports ended up taking much longer than anticipated, making them less actionable than was hoped.

Because units received a rebate at the end of the fiscal year, the effective price signal was much less than the original $40 per tCO2e. Participants noted that they paid more attention to the net equivalents, which were shown on the energy report, than to the gross carbon prices, charges and rebates. The pilot report gave examples of significantly lower net carbon charges:

For example, consider a building in “redistribution” scheme, Betts House. While Betts House pays $40 per tCO₂e on 73.24 emissions, totaling $2,929.69, it receives a rebate of $4,363.33, resulting in a net rebate of $1,433.64. Dividing the net rebate of $1,433.64 by the aggregate emissions of 73.24 produces a marginal carbon price of $19.57 per tCO₂e. This value is nearly half the social cost of carbon (SCC). There is a similar result in the “target” scheme. The Lab of Epidemiology and Public Health incurs a net carbon price of $1.62 per tCO₂e, nearly one-fortieth the SCC.

This combination of information and psychology was found to reduce the price signal by a factor of ten in many cases. In the future, Yale could show “gross” charges only on the report, but after one year the participants would figure out the net equivalents. To maintain the incentives, Yale would have to raise its carbon prices, which may be politically difficult to do. Another way to solve this problem is to decouple the rebates from performance: the revenues from carbon charge would be used to reduce costs elsewhere such as fixed-rate facilities costs (e.g., custodial services and maintenance); the savings would then be allocated to the buildings on a per square-footage basis, or based on a unit’s share of university’s service charges. To create a robust price signal, it is critical that a separation between the carbon charge and rebates exists and is perceived as such by participants. Clear explanation of how the carbon charge works to participants (i.e., that the rebates are decoupled from performance) is therefore equally important.

Discussion Questions

• What information should Yale include or conduct A-B testing on as part of its new building energy and carbon report? What campus stakeholders should receive this report?
• In particular, are there benefits to showing "gross" carbon charges only versus "gross" and "net" carbon charges? How would you go about identifying whether these analytics are the most appropriate and actionable insights? Are there benefits to focus groups or A-B testing, and could these processes inform the development of this report? Could A-B testing with this report offer another opportunity for experimentation, in addition to the carbon charge? Which is more important? The price signal on the report or the fact that a decision-maker is receiving a report for the first time ever?